TREAT: Temporal and Relational Attention-Based Tensor Representation Learning for Ethereum Phishing Users

Abstract

The Ethereum blockchain platform has witnessed a surge in crypto-cybercrimes, particularly phishing attacks, resulting in significant financial losses. Analyzing the Ethereum transaction network to detect phishing users poses a set of critical challenges, including network sparsity, dynamic network fluctuations, large-scale data and significant class imbalance. Existing literature in this area primarily leverages traditional feature engineering or network representation learning to retrieve crucial information from transaction records to identify suspected users. However, these methods mainly rely on manually handcrafted features or conventional node representation learning while ignoring the inherent network sparsity and dynamic fluctuations. Hence, to alleviate these challenges, this paper introduces a novel tensor-based representation learning framework, TREAT (Temporal andRelationalAttention-basedTensor Representation Learning). TREAT models the Ethereum transaction network as a 3-dimensional tensor to preserve structural, transactional, and temporal aspects in a standalone architecture, thereby observing the rich correlation among these. The framework is coupled with a two-way self-attention mechanism alongside a rank-based tensor decomposition to comprehend the underlying evolving transaction interaction patterns while addressing the network sparsity. A Graph Neural Network layer with edge attention elevates the final representation quality, thereby yielding a 3%~4% improvement inF1-Scorewith respect to the existing baselines.